Day-04 Types of Unsupervised learning

Unsupervised learning uses self-learning algorithm. it learns without any labels.

Unsupervised machine learning methods

there are three types of unsupervised learning tasks

• Clustering : Clustering is a technique for exploring raw, unlabeled data and breaking it down into groups (or clusters) based on similarities or differences.

  It is used in a variety of applications, including customer segmentation, fraud detection, and image analysis. Clustering algorithms split data into natural groups by finding similar structures or patterns in uncategorized data.

• Association : Association rule mining is a rule-based approach to reveal interesting relationships between data points in large datasets.
  For example; You might be most familiar with these rules from the “Frequently bought together” and “People who bought this item also bought” sections on your favorite online retail shop.

  Association rules are also often used to organize medical datasets for clinical diagnoses. Using unsupervised machine learning and association rules can help doctors identify the probability of a specific diagnosis by comparing relationships between symptoms from past patient cases. Eg: MRI Reports, X-ray machines etc.